Abstract 6689: Whole genome cell-free tumor DNA mutational signatures from blood for early detection of recurrence of low stage lung adenocarcinoma
Authors: Ivy Tran, Alejandro Vargas, Reid Wilkins, Isabella Pizzillo, Kenneth Tokoro, Danielle Afterman, Tomer Lauterman, Maja Kuzman, Santiago Gonzalez, Dunja Glavas, James Smadbeck, Dillon Maloney, Jurica Levatic, Samuel Phillips, Sunil Deochand, Michael Yahalom, Ryan Ptashkin, Iman Tavassoly, Zohar Donenhirsh, Eric White, Ravi Kandasamy, Ury Alon, Paz Polak, Boris Oklander, Asaf Zviran, Matija Snuderl, Harvey I. Pass
Journal: Cancer Research (2023)

Abstract Introduction: Lung cancer remains the leading cause of cancer-related deaths. Surgery is the best option for early lung cancer, and the role of adjuvant therapy remains controversial. Liquid biopsy offers a noninvasive approach to monitor cancer burden. Targeted sequencing of circulating cell free tumor DNA (ctDNA) in blood has shown success for diagnosis; however, low tumor burden and dynamic evolution of low stage disease is challenging for targeted panels. Thus, we hypothesized that a whole genome sequencing (WGS)-derived patient specific mutational signature from a matched tumor-normal WGS can provide sensitive and specific approach to detect mutations and copy numbers in ctDNA for monitoring of lung adenocarcinoma patients. Methods: We successfully profiled 50 Stage 1 or 2 lung adenocarcinomas. ctDNA was extracted from 1-2 mL of plasma, tumor DNA was extracted from pathology tissue and normal germline DNA from the white blood cells. WGS using was performed on matched tumor and normal DNA, and ctDNA extracted from plasma. WGS coverage was 40x for matched tumor-normal and 20x for ctDNA. We derived a personalized mutational pattern for each tumor and used an AI-based error suppression model for quantification and ultra-sensitive detection of ctDNA in plasma samples. A patient-specific personalized genome-wide compendium of somatic mutations and copy numbers was established and ctDNA tested at 3 to 18 available time points during the therapy or follow up. A personalized mutational signature for detection ctDNA from WGS was quantified and the ctDNA Tumor Fraction (TF) was compared to the clinical status and time to recurrence. Results: Tumor specific signatures were derived from matched tumor-normal samples with &amp;gt;5% tumor purity and &amp;lt;30% duplications rate. Out of all patients, 33 patients showed no recurrence and 12 recurred. Tumor-specific signatures detected the presence of the tumor signature in plasma with TF as low as 10−5. Based on positive minimal residual disease in plasma, the recurrence prediction sensitivity was 0.75 and specificity 0.82, with positive predictive value of 0.6 and negative predictive value 0.9. WGS ctDNA predicted recurrence with a median lead time of 508 days before clinical/imaging recurrence. In one case we were able to identify the second primary by deconvoluting known and novel ctDNA mutations. ctDNA mutational profiles enabled identification of smoking mutational signature matching clinical history, and APOBEC and ageing signatures as well as tumor mutational burden. Conclusions: Patient-specific WGS tumor signature from plasma derived ctDNA enables specific and ultrasensitive tracking of minimal residual disease in low stage lung adenocarcinoma patients. Molecularly positive status can be used to predict recurrence and identify patients with clinical low stage disease that may benefit from adjuvant therapy. Citation Format: Ivy Tran, Alejandro Vargas, Reid Wilkins, Isabella Pizzillo, Kenneth Tokoro, Danielle Afterman, Tomer Lauterman, Maja Kuzman, Santiago Gonzalez, Dunja Glavas, James Smadbeck, Dillon Maloney, Jurica Levatic, Samuel Phillips, Sunil Deochand, Michael Yahalom, Ryan Ptashkin, Iman Tavassoly, Zohar Donenhirsh, Eric White, Ravi Kandasamy, Ury Alon, Paz Polak, Boris Oklander, Asaf Zviran, Matija Snuderl, Harvey I. Pass. Whole genome cell-free tumor DNA mutational signatures from blood for early detection of recurrence of low stage lung adenocarcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6689.